文献类型：期刊文献

中文题名：肌音信号模式识别在嵌入式实时手语翻译系统中的应用

英文题名：Application of Mechanomyogram’s Pattern Recognition in Real-Time Sign Language Translation Embedded System

作者：王新平[1];夏春明[1];章含阳[1]

机构：[1]华东理工大学机械与动力工程学院,上海200237

年份：2021

卷号：36

期号：2

起止页码：304

中文期刊名：数据采集与处理

外文期刊名：Journal of Data Acquisition and Processing

收录：CSTPCD;;Scopus;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

语种：中文

中文关键词：肌音信号;模式识别;手语实时识别;反向传播神经网络;嵌入式系统

外文关键词：mechanomyogram(MMG);pattern recognition;real-time sign language recognition;back propagation neural network(BPNN);embedded systems

摘要：基于肌音信号(Mechanomyogram,MMG)的模式识别是指采集MMG信号,应用机器学习算法进行动作识别的过程。为了实现手语实时分类,本文采用基于STM32芯片的轻量级嵌入式设备,控制双轴加速度传感器采集了前臂3块肌肉的6通道MMG,应用反向传播神经网络(Back propagation neural network,BPNN)算法建立分类模型,并将模型参数导入嵌入式系统中,实现算法的移植。实验结果表明该嵌入式系统可实现30种手语的实时识别,模型自测识别率达99.6%,实时识别中可达97.5%,每个动作分类所需时间少于0.52 ms,满足实时性要求,具有较高的实际应用价值。本文的研究结果可应用于人体康复工程,哑语翻译器,义肢控制等领域。
Mechanomyogram(MMG)-based pattern recognition refers to the process of collecting MMG bands and applying machine learning algorithms to perform motion recognition.To realize the real-time classification of sign language motions,six-channel MMG signals of three muscles on forearm are collected by dual-axis acceleration sensors which are controlled by lightweight embedded device with STM32 chip.The back propagation neural network(BPNN)algorithm is used to establish recognition classification models,where the parameters are extracted and put into the embedded system to transfer BPNN algorithm.The embedded device can accomplish real-time recognition of 30 kinds of sign language motions,with the self-test accuracy up to 99.6%and the accuracy of real-time recognition up to 97.5%.Moreover,the classification time for each motion is less than 0.52 ms,satisfying the real-time recognition condition.The results can be applied to the fields of rehabilitation engineering,sign language translator,and prosthetic control,etc.